headshot of Joseph Yun

Joseph Yun

Program Leader: Level 4 Information Technology
Research Professor
joe.yun@pitt.edu
Misinformation Research Lab Electrical and Computer Engineering

overview

Joseph T. Yun serves as the Artificial Intelligence and Innovation Architect at the University of Pittsburgh, where he also holds the position of Research Professor of Electrical and Computer Engineering in the Swanson School of Engineering. His research focuses on building advanced AI and machine learning systems and technologies, with an emphasis on their applications across business and societal contexts. Yun is actively engaged in exploring cutting-edge technologies like blockchain to enhance the practical implementation of AI. Additionally, he is committed to examining the broader societal impacts of these emerging technologies.

about

PhD in Informatics, University of Illinois at Urbana Champaign, 2018

MS in Advertising, University of Illinois at Urbana Champaign, 2014

BS in Computer Science, University of Illinois at Urbana Champaign, 2001

Wen, T.J., Chuan, C.H., Anghelcev, G., Sar, S., Yun, J.T., & Xu, Y. (2024). Infusing Affective Computing Models into Advertising Research on Emotions. JOURNAL OF ADVERTISING, 53(5), 710-731.Taylor & Francis. doi: 10.1080/00913367.2024.2409254.

Yun, J.T., & Strycharz, J. (2023). Building the Future of Digital Advertising One Block at a Time: How Blockchain Technology Can Change Advertising Practice and Research. JOURNAL OF CURRENT ISSUES AND RESEARCH IN ADVERTISING, 44(1), 24-37.Taylor & Francis. doi: 10.1080/10641734.2022.2090464.

Donelson, C., Sutter, C., Pham, G.V., Narang, K., Wang, C., & Yun, J.T. (2021). Using a Machine Learning Methodology to Analyze Reddit Posts regarding Child Feeding Information. Journal of Child and Family Studies, 30(5), 1290-1298.Springer Nature. doi: 10.1007/s10826-021-01923-5.

Pamuksuz, U., Yun, J.T., & Humphreys, A. (2021). A Brand-New Look at You: Predicting Brand Personality in Social Media Networks with Machine Learning. Journal of Interactive Marketing, 56(1), 1-15.SAGE Publications. doi: 10.1016/j.intmar.2021.05.001.

Sutter, C., Pham, G.V., Yun, J.T., Narang, K., Sundaram, H., & Fiese, B.H. (2021). Food parenting topics in social media posts: Development of a coding system, examination of frequency of food parenting concepts, and comparison across Reddit and Facebook. Appetite, 161, 105137.Elsevier. doi: 10.1016/j.appet.2021.105137.

Yun, J.T., Duff, B.R.L., Vargas, P.T., Sundaram, H., & Himelboim, I. (2020). Computationally Analyzing Social Media Text for Topics: A Primer for Advertising Researchers. Journal of Interactive Advertising, 20(1), 47-59.Taylor & Francis. doi: 10.1080/15252019.2019.1700851.

Yun, J.T., Segijn, C.M., Pearson, S., Malthouse, E.C., Konstan, J.A., & Shankar, V. (2020). Challenges and Future Directions of Computational Advertising Measurement Systems. JOURNAL OF ADVERTISING, 49(4), 446-458.Taylor & Francis. doi: 10.1080/00913367.2020.1795757.

Yun, J.T., Vance, N., Wang, C., Marini, L., Troy, J., Donelson, C., Chin, C.L., & Henderson, M.D. (2020). The Social Media Macroscope: A science gateway for research using social media data. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 111, 819-828.Elsevier. doi: 10.1016/j.future.2019.10.029.

Yun, J.T., Duff, B.R.L., Vargas, P., Himelboim, I., & Sundaram, H. (2019). Can we find the right balance in cause-related marketing? Analyzing the boundaries of balance theory in evaluating brand-cause partnerships. PSYCHOLOGY & MARKETING, 36(11), 989-1002.Wiley. doi: 10.1002/mar.21250.

Yun, J.T., Pamuksuz, U., & Duff, B.R.L. (2019). Are we who we follow? Computationally analyzing human personality and brand following on Twitter. INTERNATIONAL JOURNAL OF ADVERTISING, 38(5), 776-795.Taylor & Francis. doi: 10.1080/02650487.2019.1575106.

Yun, J.T., & Duff, B.R.L. (2017). Is utilizing themes an effective scheme? Choice overload and categorization effects within an extensive online choice environment. COMPUTERS IN HUMAN BEHAVIOR, 74, 205-214.Elsevier. doi: 10.1016/j.chb.2017.04.038.

Wang, C., Marini, L., Chin, C.L., Vance, N., Donelson, C., Meunier, P., & Yun, J.T. (2019). Social Media Intelligence and Learning Environment: an Open Source Framework for Social Media Data Collection, Analysis and Curation. In 2019 15th International Conference on eScience (eScience), 00, (pp. 252-261).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/escience.2019.00035.